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- LISST-Deep | Categories | Sequoia Scientific

distribution in µl l in n x 32 matrix Read more How to Compute the Mean Particle Diameter from a LISST Volume Distribution March 26 2010 How to Compute the Mean Particle Diameter from a LISST Volume Distribution Sequoia March 26 2010 In the table below the computation of the mean particle size from a LISST volume distribution is outlined Column A is the size class Read more mat2tt m May 4 2009 mat2tt m Sequoia May 4 2009 The mat2tt m script converts a matrix with raw data in ASCII format into a binary file with DAT extension that can be read into the LISST SOP for processing It is useful for converting LOG files created Read more datenumfromdata m November 5 2008 datenumfromdata m Sequoia November 5 2008 Function to compute date in a year from columns 39 and 40 from the dat file read using tt2mat User must input columns 39 and 40 as an nx2 matrix together with the year Output is Read more LISST concentration limits August 29 2008 What are the concentration limits for a LISST instrument There are really 2 answers to this question The short one and the more elaborate one The short one follows here the elaborate one a bit further down this webpage At the Read more Previous Next More in About About Suspended Sediment Concentration and Particle Sizing Methods Course Staff News PiE Conference 2014 PiE Conference 2014 Customer List Newsletter Contact International Distributors Our Newsletter Sign up to get the latest news on Sequoia and our products Home Products Product Overview LISST Instruments LISST 100X LISST ABS LISST Portable XR LISST Holo LISST Deep LISST StreamSide LISST STX LISST Hydro LISST Infinite LISST VSF LISST STOKES Optical VSF Sensors LISST SL LISST 25X FlowControl Instruments FlowControl Lab FlowControl

Original URL path: http://www.sequoiasci.com/article_category/lisst-deep/page/3/ (2016-02-13)

Open archived version from archive - Converting LISST volume concentration to mass concentrations | Sequoia Scientific

the particles with the density of the particles For a suspension of fully dispersed unflocculated mineral grains the density of all particles will be 2 65 g cm3 or very close to this value In this case the VC with units of µl l could simply be multiplied with 2 65 in order to yield mass concentration in units of mg l Major problems arise when the density of the particles in suspension are not all the same There are several reasons why this might be Flocculation If particles flocculate their density decreases The more they flocculate the smaller the density A floc with a diameter of a few hundred micro meters can easily have a density very close to that of water 1 g cm3 because most of its volume is water Mixture of organic inorganic particles If the suspended particles are made up of a mixture of organic e g diatoms and inorganic particles then the densities will differ as the organic particles tend to have much smaller densities than the mineral grains Different minerals If the suspended particles in your sample are made of of minerals with varying densities the same thing will happen It is possible to occasionally take a water sample filter it get the mass concentration and then compare that to the VC from the same sample thereby getting an average density for the particles for that sample However if just some of the particles are flocculated then using an average density to convert volume to mass will invariably cause an overestimation of the mass in the larger size classes and a general overestimation of the mass concentration Of course for the exact sample that is being used to obtain the relationship everything will be OK The best way to convert VC to mass concentration is to take a water sample for mass concentration now and then and divide the mass concentration with the volume concentration from the LISST in order to get the average density of the particles in that paricular sample Do this over time and you will get an idea of how much the density of the particles generally varies If the variability is small then do an average of all the densities and use this value to for future runs One may find a seasonal variability of the densities with lower densities in the summer and higher in the winter due to more organic particles in the summer in which case it would be necessary to use a seasonally varying density to convert LISST volume to mass The following articles may be of interest if you are interested in learning more about this topic Fennessy et al 1994 INSSEV An instrument to measure the size and settling velocity of flocs in situ Marine Geology 117 107 117 Fennessy et al 1997 Estimation of settling flux spectra in estuaries using INSSEV In Cohesive Sediments Eds Burt Parker Watts John Wiley Sons pp 87 104 Fugate D and Chant B 2006 Aggregate

Original URL path: http://www.sequoiasci.com/article/converting-lisst-volume-concentration-to-mass-concentrations/ (2016-02-13)

Open archived version from archive - LISST-Portable | Categories | Sequoia Scientific

suspension contains particles outside the measurement range The short answer Read more Comparing volume concentration and mass concentration April 7 2010 How does µl l and mg l compare Sequoia April 7 2010 A question that Sequoia often gets asked is How does the volume concentration in units of µl l from the LISST compare to mass concentration in mg l And how do I convert Read more compute mean m March 29 2010 Compute mean Sequoia March 29 2010 Function to quickly compute mean diameter in µm from VD data usage diameters compute mean vd type transmission where vd is volume distribution in µl l in n x 32 matrix Read more How to Compute the Mean Particle Diameter from a LISST Volume Distribution March 26 2010 How to Compute the Mean Particle Diameter from a LISST Volume Distribution Sequoia March 26 2010 In the table below the computation of the mean particle size from a LISST volume distribution is outlined Column A is the size class Read more Processing LISST StreamSide and LISST Portable data in MATLAB May 22 2009 Sequoia May 22 2009 NOTE These instructions apply to the LISST Portable but not to the newer LISST Portable XR I INTRODUCTION II SOFTWARE FUNCTIONS II 1 Using the invert p processing file II 2 Using Matlab functions for detailed Read more Previous Next More in About About Suspended Sediment Concentration and Particle Sizing Methods Course Staff News PiE Conference 2014 PiE Conference 2014 Customer List Newsletter Contact International Distributors Our Newsletter Sign up to get the latest news on Sequoia and our products Home Products Product Overview LISST Instruments LISST 100X LISST ABS LISST Portable XR LISST Holo LISST Deep LISST StreamSide LISST STX LISST Hydro LISST Infinite LISST VSF LISST STOKES Optical VSF Sensors LISST SL

Original URL path: http://www.sequoiasci.com/article_category/lisst-portable/page/3/ (2016-02-13)

Open archived version from archive - LISST-SL | Categories | Sequoia Scientific

l from the LISST compare to mass concentration in mg l And how do I convert Read more compute mean m March 29 2010 Compute mean Sequoia March 29 2010 Function to quickly compute mean diameter in µm from VD data usage diameters compute mean vd type transmission where vd is volume distribution in µl l in n x 32 matrix Read more How to Compute the Mean Particle Diameter from a LISST Volume Distribution March 26 2010 How to Compute the Mean Particle Diameter from a LISST Volume Distribution Sequoia March 26 2010 In the table below the computation of the mean particle size from a LISST volume distribution is outlined Column A is the size class Read more mat2tt m May 4 2009 mat2tt m Sequoia May 4 2009 The mat2tt m script converts a matrix with raw data in ASCII format into a binary file with DAT extension that can be read into the LISST SOP for processing It is useful for converting LOG files created Read more datenumfromdata m November 5 2008 datenumfromdata m Sequoia November 5 2008 Function to compute date in a year from columns 39 and 40 from the dat file read using tt2mat User must input columns 39 and 40 as an nx2 matrix together with the year Output is Read more Previous Next More in About About Suspended Sediment Concentration and Particle Sizing Methods Course Staff News PiE Conference 2014 PiE Conference 2014 Customer List Newsletter Contact International Distributors Our Newsletter Sign up to get the latest news on Sequoia and our products Home Products Product Overview LISST Instruments LISST 100X LISST ABS LISST Portable XR LISST Holo LISST Deep LISST StreamSide LISST STX LISST Hydro LISST Infinite LISST VSF LISST STOKES Optical VSF Sensors LISST SL LISST 25X FlowControl Instruments

Original URL path: http://www.sequoiasci.com/article_category/lisst-sl/page/3/ (2016-02-13)

Open archived version from archive - LISST-STX | Categories | Sequoia Scientific

sediment in situ In order to accomplish this we have fitted Read more The Influence of Particles Outside the Size Range of the LISST February 23 2011 The Influence of Particles Outside the Size Range of the LISST Sequoia February 23 2011 A question that Sequoia often gets asked is What happens when the suspension contains particles outside the measurement range The short answer Read more Comparing volume concentration and mass concentration April 7 2010 How does µl l and mg l compare Sequoia April 7 2010 A question that Sequoia often gets asked is How does the volume concentration in units of µl l from the LISST compare to mass concentration in mg l And how do I convert Read more compute mean m March 29 2010 Compute mean Sequoia March 29 2010 Function to quickly compute mean diameter in µm from VD data usage diameters compute mean vd type transmission where vd is volume distribution in µl l in n x 32 matrix Read more How to Compute the Mean Particle Diameter from a LISST Volume Distribution March 26 2010 How to Compute the Mean Particle Diameter from a LISST Volume Distribution Sequoia March 26 2010 In the table below the computation of the mean particle size from a LISST volume distribution is outlined Column A is the size class Read more Previous Next More in About About Suspended Sediment Concentration and Particle Sizing Methods Course Staff News PiE Conference 2014 PiE Conference 2014 Customer List Newsletter Contact International Distributors Our Newsletter Sign up to get the latest news on Sequoia and our products Home Products Product Overview LISST Instruments LISST 100X LISST ABS LISST Portable XR LISST Holo LISST Deep LISST StreamSide LISST STX LISST Hydro LISST Infinite LISST VSF LISST STOKES Optical VSF Sensors LISST SL

Original URL path: http://www.sequoiasci.com/article_category/lisst-stx/page/3/ (2016-02-13)

Open archived version from archive - datenumfromdata.m | Sequoia Scientific

using tt2mat User must input columns 39 and 40 as an nx2 matrix together with the year Output is the date number in standard MATLAB format Usage function realdatenum datenumfromdata year data3940 where year is the year of the very first measurement data3940 is a nx2 matrix with the first column equal to column 39 and the second column equal to column 40 from the dat file e g data 39 40 OAM October 30 2008 function realdatenum datenumfromdata year data3940 if nargin 2 error There must be 2 input arguments year and a matrix with 2 columns end if size data3940 2 2 error There must be 2 columns in the input matrix data3940 end compute days hours minutes seconds days fix data3940 1 100 hours data3940 1 100 days minutes fix data3940 2 100 seconds data3940 2 100 minutes years year ones size data3940 1 1 creat a vector with the initial year NewYear find diff days 0 find negative differences in daynumber indicate deployment over new year if isempty NewYear If we have a new year deployment years NewYear 1 end year 1 the year after new year is one higher than the inital year we assume that the deployment doesn t span more than one new year end realdatenum datenum years 1 days hours minutes seconds Questions about this MATLAB script Email us More in Library Library Articles Technical Papers Standards Our Newsletter Sign up to get the latest news on Sequoia and our products Home Products Product Overview LISST Instruments LISST 100X LISST ABS LISST Portable XR LISST Holo LISST Deep LISST StreamSide LISST STX LISST Hydro LISST Infinite LISST VSF LISST STOKES Optical VSF Sensors LISST SL LISST 25X FlowControl Instruments FlowControl Lab FlowControl Sub Radiative Transfer Models EcoLight S HydroLight Accessories How to

Original URL path: http://www.sequoiasci.com/article/datenumfromdata-m/ (2016-02-13)

Open archived version from archive - Processing LISST-100 and LISST-100X data in MATLAB | Sequoia Scientific

of progress Outputs are vd volume distribution NOT CALIBRATED WITH VCC dias the midpoint of the size bins for the 8 32 size classes for the appropriate instrument inversion type and laser color In order to convert the volume distribution into calibrated units you must divide vd by the Volume Conversion Constant VCC which can be found as the 4th number in the InstrumentData txt file that was provided with your instrument on your ship disk This is done using the script vdcorr m The syntax for converting vd to calibrated vd is vd vdcorr vd VCC flref lref where vd is the vd from invert p VCC is the Volume Conversion Constant for your instrument flref is the factory laser reference value for you instrument element 36 in your factory zsc file lref is the laser reference value during measurement For LISST 100X LISST STX and LISST Deep it is element 36 in the DAT file If you have only one measurement in your DAT file you can specify this as data 36 otherwise specify it as data 36 The power of this function is that it processes all data at once producing the volume distribution for the entire data file Once done you can proceed with plotting your results The weakness of this function is that the user does not see intermediate results The invert p processing script assumes that the data and background files are perfect Often this is the case However scientists are always well advised to look at their data in detail For this several functions are provided that do partial processing These are described next II 2 Using Matlab functions for detailed processing Loading your files The most basic function is tt2mat m It reads a binary file background file or data file dat extension It is not suitable for ASCII files To use data tt2mat datafilename 40 However in case you are loading a LISST 100X binary data file one further step is necessary data 1 32 data 1 32 10 This is done in order to compensate for the raw data being saved at high gain or having been multiplied by 10 prior to storing on the data logger cf introduction zsc tt2mat background file name 40 Again if you are loading a LISST 100X binary background file you must compensate for the multiplication by a factor of 10 zsc 1 32 zsc 1 32 10 The resulting variables now have a dimension of row 40 where row is the number of records in the binary file To compute a mean as you would want to do with the zsc from a binary file type zsc mean zsc When reading an ASCII file can be asc or log extension use the following command data load datafilename zsc load background file name Specify the full file name including the extension Note that now one need not specify the number of variables per record 40 The ASCII files are typically created by processing the binary files in the LISST SOP Windows software and DO NOT need to be divided by 10 as the Windows software already performs this operation It is of course possible to load a binary data file and an ASCII background file or vice versa and use them together In this case for a LISST 100X data tt2mat datafilename dat 40 data 1 32 data 1 32 10 zsc load background file name asc Viewing the raw data files These functions permit you to look at the details of your data For example you may look at the time series of the background file only if this is a binary file the asc file already is averaged before writing to computer memory Viewing can be done by typing plot zsc 1 32 or plot zsc 1 32 The first of these would display a time series of all 32 ring detectors the latter will show all the records as backgrounds across the 32 rings In the time series spikes in rings may be revealed suggesting bubbles or contamination during collecting a background file In the second display the background pattern and its variability during the data acquisition will be revealed A background file should not show high variability i e less than a few counts Variability in backgrounds comes from contamination For example you may plot the standard deviation of the background light on each of the 32 rings with a simple command plot std zsc 1 32 As noted if the background file is of the asc or log type it is only a 40 variable file The variables are the averages of multiple scans during data acquisition This command will not work with zsc from an ASCII file You may use the same commands to view a time series of your in situ data file Generally we advise that you first look at the laser transmission and laser reference i e variables 33 and 36 as follows plot data 33 pause 2 plot data 36 You can plot these variables simultaneously with any others e g plot data 33 36 will show the laser transmitted and reference power time series Matlab follows a color scheme when multiple variables are plotted The color scheme follows the order blue green red cyan To compute and plot the optical transmission for your data series r zsc 33 zsc 36 Use this form after zsc is loaded as an ASCII file or after taking the mean of the variable zsc if loading the background binary file Now the transmission is cf Eq 1 tau data 33 r data 36 Plotting the time series of your transmission record is simple plot tau or to put symbols at the data points see Matlab guide for allowed symbols plot tau To compute the net scattering by de attenuating the measurements and subtracting the background cf Eq 4 row col size data for i 1 row scat i data i 1 32 tau i zsc 1 32

Original URL path: http://www.sequoiasci.com/article/processing-lisst-100-and-lisst-100x-data-in-matlab/ (2016-02-13)

Open archived version from archive - getscat.m | Sequoia Scientific

original getscat and getscatX by YCA Merged by OAM April 14 2008 to become version 2 0 September 29 2008 Added option to load datafiles as log files OAM v 2 05 August 18 2010 Added option to output cscat if the ringarea file is specified OAM v 2 10 September 17 2010 Added check to see if zscat file in dat format had more than 1 row OAM v 2 11 March 10 2011 Forced negative cscats to 0 OAM function scat tau zsc data cscat getscat datafile zscfile X ringareafile check to see if at least 3 input arguments exists if nargin 3 error You must specify at least datafile zscfile and X end check to see if cscat is being output if ringarea file is not supplied if nargin 4 if nargout 4 error You have not specified a ringarea filename yet have specified cscat as output You must specify a ringarea file if you wish cscat to be output end end if nargin 4 dcal load ringareafile end 1 Load the zscat file in either binary or ASCII format pathstr name ext fileparts zscfile get datafile info if sum strcmp ext asc ASC 0 if the zscat file is an ASCII file zsc load zscfile go ahead and load it a size zsc 1 get the number of rows in zsc if a 1 if the zsc file is in ASCII format there should only be one row and 40 columns zsc zsc if not transpose end else if the zscat file is NOT an ASCII file then it can only be a dat file zsc tt2mat zscfile 40 so read it using tt2mat if X 1 do we have LISST 100X data format zsc 1 32 zsc 1 32 10 then divide rings 1 32 by 10 end if size zsc 1 1 are there more than 1 row in the zsc dat file OAM 9 17 10 zsc mean zsc compute mean so that zsc always is a 1 x 40 vector end end r zsc 33 zsc 36 compute the laser power laser reference ratio to adjust for drift in laser output power over time 2 Read the binary data file pathstr name ext fileparts datafile get datafile info if sum strcmp ext log LOG 0 if the data file is a log file data load datafile load it right away else data tt2mat datafile 40 read the binary data file using tt2mat if X 1 do we have LISST 100X data format data 1 32 data 1 32 10 then divide rings 1 32 by 10 end end note log files are by default stored in LISST 100 format so they do not need to be divided by 10 after being loaded 3 Compute optical transmission raw scattering and cscat if applicable tau data 33 r data 36 compute optical transmission taking the eventual drift in laser power into account row size data 1 scat zeros row 32 pre allocate scat matrix

Original URL path: http://www.sequoiasci.com/article/getscat-m/ (2016-02-13)

Open archived version from archive